Molecular imaging identification of changes in the cellular structures indicative of disease remains a key to the better understanding in medicinal science. Microscopy applications are applicable to microbiology (e.g., gram staining, etc.), plant tissue culture, animal cell culture (e.g. phase contrast microscopy, etc.), molecular biology, immunology (e.g., ELISA, etc.), cell biology (e.g., immunofluorescence, chromosome analysis, etc.), confocal microscopy, time-lapse and live cell imaging, series and three-dimensional imaging.
There have been advances in confocal microscopy that have unraveled many of the secrets occurring within the cell and the transcriptional and translational level changes can be detected using fluorescence markers. The advantage of the confocal approach results from the capability to image individual optical sections at high resolution in sequence through the specimen. However, there remains a need for systems and methods for digital processing of images of pathological tissue that provide accurate analysis of pathological tissues, at a relatively low cost.
It is a desirable goal in digital pathology to obtain high resolution digital images for viewing in a short period of time. Current manual methods whereby the pathologist views a slide through the ocular lens of a microscope allows a diagnosis upon inspection of cell characteristics or count of stained cells vs. unstained cells. Automated methods are desirable whereby digital images are collected, viewed on high resolution monitors and may be shared and archived for later use. It is advantageous that the digitization process be accomplished efficiently at a high throughput and with high resolution and high quality images.
In conventional virtual microscopy systems, imaging techniques can produce individual images that may be significantly out of focus over much of the image. Conventional imaging systems are restricted to a single focal distance for each individual snapshot taken by a camera, thus, each of these “fields of view” has areas that are out of focus when the subject specimen being scanned does not have a uniform surface. At the high magnification levels employed in virtual microscopy, specimens with a uniform surface are extremely rare.
Conventional systems use a pre-focusing technique to address the high proportion of out-of-focus images that is based on a two step process that includes: 1) determining, in a first pass, the best focus at an array of points, separated by n image frames, arranged on a two-dimensional grid laid on the top of a tissue section; and 2) in another pass, moving to each focus point and acquire an image frame. For points between these best focus points, the focus is interpolated. While this two step process may reduce or even eliminate out-of-focus images, the process results in a significant loss in the speed of acquiring the tiled images.
Accordingly, it would be desirable to provide a system that overcomes the significant problems inherent in conventional imaging systems and efficiently provides focused, high quality images at a high throughput.